1. © Prof David J Harper 2004 The Challenge of Finding Information in (Long) e-Theses David J Harper The Robert Gordon University Smart Web Technologies Centre School of Computing Aberdeen, Scotland.

2. © Prof David J Harper 2004 Lifecycle of an e-Thesis u Do research u Write up research u Production (of e-thesis) u Publish u Retrieval (search, directory lookup, …) u Delivery u Use

3. © Prof David J Harper 2004 Categorization of Reading Activities (Uses) Reading to … u … to select a document u Buying a book u Opening a webpage retrieved by search engine u Deciding to read document u … to extract/locate specific information u Finding a quotation in a book u Locating contact details on a webpage u … to reference information (more generally) u Finding supporting information for a legal case u Finding related work

4. © Prof David J Harper 2004 Categorization of Reading Activities (cont) Reading to … u … to write a document u Usually involves a complex mix of other reading activities u … to explore the information space from a given “pivot” document u Follow-up bibliographic references in a paper u Follow hypertext links in web pages u Find similar documents u … to understand a document in depth u Reading a book/paper cover-to-cover u Skimming a book/paper

5. © Prof David J Harper 2004 ProfileSkim u Developed to support retrieval within long documents u Within document retrieval tool: supports reading to extract and reading to reference u Main concept: relevance profiling based on language modelling u Replacement for the ubiquitous (and ineffective) Find-command

6. © Prof David J Harper 2004 Overview of ProfileSkim Tool “Tile” being visited Highlighted Query Terms Query input Page/file to skim

7. © Prof David J Harper 2004 Relevance Profile Meter (1) Retrieval Status Value Word position Document Relevance Profile Meter Click and visit... Tile

8. © Prof David J Harper 2004 Applications of Relevance Profiling in e-Theses u Locating information within an e-thesis u Augmenting retrievals results (snippets) returned by a search engine u Retrieve e-theses by search/query u Present ranked list of e-thesis snippets (title, author, extract, date, etc) u Augment each snippet with relevance profile (Augmenting Search Engine Results)Augmenting Search Engine Results u Dynamic cross-referencing within an e-thesis u User is reading an e-thesis u Selects text of interest u Dynamically construct relevance profile using selected text as the query

9. © Prof David J Harper 2004 Closing Remarks u ProfileSkim is an effective tool for within document retrieval, and this has been confirmed by end user experiments u There are a number of potential applications of relevance profiling in e-theses u ProfileSkim is a standalone tool, which is currently being developed as an Acrobat Reader plug-in u Range of possible reading activities suggests a need for a specialised e-thesis reading tool/system

10. © Prof David J Harper 2004 Further Information on ProfileSkim http://www.comp.rgu.ac.uk/staff/sy/msProf.htm

11. © Prof David J Harper 2004 SmartWeb - A few words from my sponsor u Centre of excellence on intelligent computing technologies and their application to web-based systems and services u Basic and applicable research targeted at: e- Government, e-Learning, e-Health and e-Business u Located at the Robert Gordon University, Aberdeen u Seeking collaborators, commercialisation partners, exchanges, short- and longer term visitors, PhD students u www.smartweb.rgu.ac.uk www.smartweb.rgu.ac.uk

12. © Prof David J Harper 2004 Augmenting Search Engine Results Typical Search Engine User Interface Augmented Search Results

13. © Prof David J Harper 2004 Relevance Profiling Process P(query | window) Tile max -> tile RSV Sliding window